SoroushVahidi/scidocs huggingface dataset:
Applying QR code in augmented reality applications
(2009)

In this paper we present an augmented reality (AR) application based on the QR Code. The system can extract the information embedded in a QR Code and show the information in an extended 3D form with the QR Code being the traditional AR marker. Traditional AR systems often use a particularly designed pattern (the marker) to recover the 3D scene structure and identify the object to be displayed on the scene. In these systems, the marker is used only for tracking and identification. They do not convey any other information. Consequently, the applications of these systems are limited and often a registration procedure is required for a new marker.
 QR Code has the advantage of large information capacity and is similar to an AR marker in appearance. Thus, more interesting and useful applications can be developed by combining the QR Code with the traditional AR system. In this paper, we combine these two techniques to develop a product demo system. A QR Code is pasted on the package of a product and then a 3D virtual object is displayed on the QR Code. This system allows the customer to visualize the product via a more direct and interactive way. Our system demonstrates the success of using QR Code as the AR marker to a particular application and we believe it can bring more convenient to our life in the future.
Combining CPU and GPU architectures for fast similarity search
(2012)

The Signature Quadratic Form Distance on feature signatures represents a flexible distance-based similarity model for effective content-based multimedia retrieval. Although metric indexing approaches are able to speed up query processing by two orders of magnitude, their applicability to large-scale multimedia databases containing billions of images is still a challenging issue. In this paper, we propose a parallel approach that balances the utilization of CPU and many-core GPUs for efficient similarity search with the Signature Quadratic Form Distance. In particular, we show how to process multiple distance computations and other parts of the search procedure in parallel, achieving maximal performance of the combined CPU/GPU system. The experimental evaluation demonstrates that our approach implemented on a common workstation with 2 GPU cards outperforms traditional parallel implementation on a high-end 48-core NUMA server in terms of efficiency almost by an order of magnitude. If we consider also the price of the high-end server that is ten times higher than that of the GPU workstation then, based on price/performance ratio, the GPU-based similarity search beats the CPU-based solution by almost two orders of magnitude. Although proposed for the SQFD, our approach of fast GPU-based similarity search is applicable for any distance function that is efficiently parallelizable in the SIMT execution model.
Aircraft angle of attack and air speed detection by redundant strip pressure sensors
(2004)

Air speed and angle of attack are fundamental parameters in the control of flying bodies. Conventional detection techniques use sensors that may protrude outside the aircraft, be too bulky for installation on small UAVs, or be excessively intrusive. We propose a novel readout methodology where flight parameters are inferred from redundant pressure readings made by capacitive strip sensors directly applied on the airfoil skin. Redundancy helps lower the accuracy requirements on the individual sensors. A strategy for combining sensor data is presented with an error propagation analysis. The latter enables foreseeing the precision by which the flight parameters can be detected. The methodology has been validated by fluid dynamic simulation and a sample case is illustrated.
Visual saliency based on multiscale deep features
(2015)

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.
Deep Saliency with Encoded Low Level Distance Map and High Level Features
(2016)

Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1 1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
(2018)

Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.
Symbiotic Segmentation and Part Localization for Fine-Grained Categorization
(2013)

We propose a new method for the task of fine-grained visual categorization. The method builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations. The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the instance (e.g. bird) in each image. Both segmentation and part localizations are then used to encode the image content into a highly-discriminative visual signature. The model is symbiotic in that part discovery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (e.g. part layout). Our model builds on top of the part-based object category detector of Felzenszwalb et al., and also on the powerful Grab Cut segmentation algorithm of Rother et al., and adds a simple spatial saliency coupling between them. In our evaluation, the model improves the categorization accuracy over the state-of-the-art. It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently.
Overhead line fault detection using GSM technology
(2017)

In this paper, a novel technique for the fault detection, classification & protection of transmission lines is proposed. The proposed system uses different protective equipment's, voltage sense section, microcontroller section, LED display section & GSM (global system for mobile communication) module. The faults like all series (1LO, 2LO, 3LO etc.) & shunt faults (L-G, L-L, L-L-G, L-L-L, L-L-L-G) get detected & classified according to characteristics condition of current & voltage at the occurrence of fault in the three phase overhead lines. The sensed signals are given to microcontroller for detection & classification of faults. Also wireless mobile communication technique i.e. GSM is used simultaneously to send message to responsible person on mobile. Type of fault gets displayed on fault display section. Simultaneously fault gets interrupted using protective devices. (fuse, circuit breakers etc.)
Complete 3D Scene Parsing from an RGBD Image
(2018)

One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes. We parse both the visible and occluded portions of the scene and all observable objects, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scenes. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and spatial consistency. We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks to classify regions and retrieve objects with similar shapes. We demonstrate the performance of our method on our newly annotated NYUd v2 dataset (Silberman et al., in: Computer vision-ECCV, 2012, pp 746–760, 2012) with detailed 3D shapes.
Compaction Management in Distributed Key-Value Datastores
(2015)

Compactions are a vital maintenance mechanism used by datastores based on the log-structured merge-tree to counter the continuous buildup of data files under update-intensive workloads. While compactions help keep read latencies in check over the long run, this comes at the cost of significantly degraded read performance over the course of the compaction itself. In this paper, we offer an in-depth analysis of compaction-related performance overheads and propose techniques for their mitigation. We offload large, expensive compactions to a dedicated compaction server to allow the datastore server to better utilize its resources towards serving the actual workload. Moreover, since the newly compacted data is already cached in the compaction server’s main memory, we fetch this data over the network directly into the datastore server’s local cache, thereby avoiding the performance penalty of reading it back from the filesystem. In fact, pre-fetching the compacted data from the remote cache prior to switching the workload over to it can eliminate local cache misses altogether. Therefore, we implement a smarter warmup algorithm that ensures that all incoming read requests are served from the datastore server’s local cache even as it is warming up. We have integrated our solution into HBase, and using the YCSB and TPC-C benchmarks, we show that our approach significantly mitigates compaction-related performance problems. We also demonstrate the scalability of our solution by distributing compactions across multiple compaction servers.
Massively Parallel A* Search on a GPU
(2015)

A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fashion. Our experiments have demonstrated that the GPU-accelerated A* search is efficient in solving multiple real-world search tasks, including combinatorial optimization problems, pathfinding and game solving. Compared to the traditional sequential CPU-based A* implementation, our GPU-based A* algorithm can achieve a significant speedup by up to 45x on largescale search problems.
Portable and Efficient Auto-vectorized Bytecode: a Look at the Interaction between Static and JIT Compilers
(2010)

Heterogeneity is a confirmed trend of computing systems. Bytecode formats and just-in-time compilers have been proposed to deal with the diversity of the platforms. By hiding architectural details and giving software developers a unified view of the machine, they help improve portability and manage the complexity of large software. Independently, careful exploitation of SIMD instructions has become crucial for the performance of many applications. However, auto-vectorizing compilers need detailed information about the architectural features of the processor to generate efficient code. We propose to reconcile the use of architecture neutral bytecode formats with the need to generate highly efficient vectorized native code. We make three contributions. 1) We show that vectorized bytecode is a viable approach that can deliver portable performance in the presence of SIMD extensions, while incurring only minor penalty when SIMD is not supported. In other words, the information that a loop can be vectorized is the vectorized loop itself. 2) We analyze the interaction between the static and just-in-time compilers and we derive conditions to deliver performance. 3) We add vectorization capabilities to the CLI port of the GCC compiler.
Real-time 3D Walking Pattern Generation for a Biped Robot with Telescopic Legs
(2001)

Meltran V, a new biped robot with telescopic legs, is introduced. For 3D walking control of the robot we analyze the dynamics of a three-dimensional inverted pendulum in which motion is constrained to move along an arbitrarily de ned plane. From this analysis we obtain simple linear dynamics, the ThreeDimensional Linear Inverted Pendulum Mode (3DLIPM). Using a real-time control method based on 3DLIPM, the Meltran V robot successfully demonstrated 3D dynamic walking without the use of any prepared trajectories.
Review on cyber-physical systems
(2017)

Cyber-physical systems U+0028 CPS U+0029 are complex systems with organic integration and in-depth collaboration of computation, communications and control U+0028 3C U+0029 technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS.
{\Omega}-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks
(2017)

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42) and healthy control subjects (N = 21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.
Two cases of hymenal scars occurred by child rape
(2017)

Children who have been raped some years back may have hymenal scars. However, medical professionals are not accustomed in assessing these scars because of the lack of experience in performing physical examinations of the external genitalia of children who suffered from rape some years back. Moreover, the importance of physical examination of the victim's external genitalia is sometimes overlooked. Two cases of rape victims with hymenal scars who visited Daegu Child Sexual Abuse Response Center several years after their first sexual abuse along with a literature review are presented here.
Distributed Power Control for the Downlink of Multi-Cell NOMA Systems
(2017)

This paper investigates the power control problem for the downlink of a multi-cell non-orthogonal multiple access system. The problem, called P-OPT, aims to minimize the total transmit power of all the base stations subject to the data rate requirements of the users. The feasibility and optimality properties of P-OPT are characterized through a related optimization problem, called Q-OPT, which is constituted by some relevant power control subproblems. First, we characterize the feasibility of Q-OPT and prove the uniqueness of its optimal solution. Next, we prove that the feasibility of P-OPT can be characterized by the Perron-Frobenius eigenvalues of the matrices arising from the power control subproblems. Subsequently, the relationship between the optimal solutions to P-OPT and that to Q-OPT is presented, which motivates us to obtain the optimal solution to P-OPT through solving the corresponding Q-OPT. Furthermore, a distributed algorithm to solve Q-OPT is designed, and the underlying iteration is shown to be a standard interference function. According to Yates’s power control framework, the algorithm always converges to the optimal solution if exists. Numerical results validate the convergence of the distributed algorithm and quantify the improvement of our proposed method over fractional transmit power control and orthogonal multiple access schemes in terms of power consumption and outage probability.
Software-based resolver-to-digital converter using the PLL tracking algorithm
(2016)

In this paper, an all-digital resolver-to-digital conversion (RDC) system using phase-locked loop (PLL) tracking algorithm is proposed. Excitation signal is obtained from PWM module of a TMS320F2812 digital signal processor (DSP). According to the excitation signal, the resolver outputs differential sine and cosine signals, which are sampled by a 16-bit peripheral AD7606 module. The rotor angle position is estimated with the PLL tracking algorithm based on DSP. The proposed RDC system is applied to a servo synchronous motor with a resolver coupled to the shaft, and the electrical error of the resolver is ±10'. Finally, responding simulations and experiments are carried out. The results demonstrate that the RDC system has the features with simple hardware structure and low cost, and high-precision resolver-to-digital conversion is achieved.
Adopting the Cognitive Complexity Measure for Business Process Models
(2006)

Business process models, often modelled using graphical languages like UML, serve as a base for communication between the stakeholders in the software development process. To fulfil this purpose, they should be easy to understand and easy to maintain. For this reason, it is useful to have measures that can give us some information about understandability, analyzability and maintainability of a business process model. Shao and Wang (2003) have proposed a cognitive complexity measure. It can be used to estimate the comprehension effort for understanding software. This paper discusses how these research results can be extended in order to analyze the cognitive complexity of graphical business process models
Challenges in Exploiting Conversational Memory in Human-Agent Interaction
(2018)

In human interactions, language is used to project and maintain a social identity over time. The way people speak with others and revisit language across repeated interactions helps to create rapport and develop a feeling of coordination between conversational partners. Memory of past conversations is the main mechanism that allows us to exploit and explore ways of speaking, given knowledge acquired in previous encounters. As such, we introduce an agent that uses its conversational memory to revisit shared history with users to maintain a coherent social relationship over time. In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat. In a study lasting 14 days with 28 users, totaling 474 interactions, we find that it is difficult to leverage the shared history with individual users and to also accommodate to expected conversational coordination patterns. We discuss the implications of this finding for long-term human-agent interaction. In particular, we highlight the importance of topic modeling and signaling explicit recall of previous episodes. Moreover, the way that users contribute to interactions requires additional adaptation, indicating a significant challenge for language interaction designers.